Advances in Applied Energy最新文献

Current challenges in nano-engineered biomass valorization: A comprehensive review from the whole procedure of biomass fermentation perspective

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-03-28 DOI: 10.1016/j.adapen.2025.100219

Zi-Tong Zhao , Jie Ding , Geng Luo , Bo-Yuan Wang , Han-Jun Sun , Bing-Feng Liu , Guang-Li Cao , Mei-Yi Bao , Nan-Qi Ren , Ji-Wei Pang , Shan-Shan Yang

Dark fermentation has been widely regarded and appraised as an efficient and green route for biohydrogen production. Lignocellulosic biomass is a readily available and abundant feedstock that could be used as a sustainable feedstock for biohydrogen generation. However, low yield of biohydrogen is an inherent issue of the bioprocess restricting its further development towards commercial margins. Recently, the supplement of nano-additives has aroused more attention as a process improvement strategy because of their ability to accelerate process performance and their strengths of low energy consumption and easy operation. Nevertheless, the utilization of nanomaterials for biomass fermentation is still in its infancy. Here we review and evaluate the feasibility of nanotechnology in each procedure of biomass to biohydrogen to improve the economic feasibility of the process. Numerous aspects such as the possibility of utilizing nanomaterials as an alternative to chemical pretreatment techniques have been highlighted in this review. Additionally, the effect of these nanostructured materials (e.g., metal-based nanoparticles, nanocomposites, and graphene-based nanomaterials) on biohydrogen fermentation and the potential functional mechanisms were also analyzed in detail. Moreover, the assessment on how the immobilized nanoparticles affect enzymatic efficiency and how well they can block inhibitory chemicals were elaborated. Further, the sustainability of biomass fermentation was assessed in terms of science economics as well as carbon neutrality to improve the overall benefits of the process. Finally, the review suggests ways in which the nano-engineered bioprocesses might be improved, as well as suggested avenues for further research.

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引用次数: 0

AI-empowered online control optimization for enhanced efficiency and robustness of building central cooling systems

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-03-22 DOI: 10.1016/j.adapen.2025.100220

Lingyun Xie , Kui Shan , Hong Tang , Shengwei Wang

Adopting Artificial Intelligence for optimizing building system controls has gained significant attention due to the growing emphasis on building energy efficiency. However, substantial gaps remain between academic research and the practical implementation of AI-based algorithms. Key factors hindering implementation include computational efficiency requirements and concerns about reliability in online applications. This paper addresses these challenges by presenting AI-empowered online control optimization technologies designed for practical implementation. A simplified deep learning-enabled Genetic Algorithm is developed to accelerate optimization processes, ensuring optimization intervals are short enough for online applications. This algorithm also significantly reduces CPU and memory usage, enabling deployment on miniaturized control station for field implementation. To enhance stability and reliability, a robust assurance scheme is introduced, which switches to expert knowledge-based control under abnormal conditions. Hardware-in-the-loop tests validate the proposed strategy's computation efficiency, control performance and operational robustness using a physical smart station controlling a simulated real-time dynamic cooling system. Test results show that the optimal control strategy achieves 7.66 % energy savings and exhibits strong operational robustness.

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引用次数: 0

Investigation of a novel separately-configured thermoelectric cooler: A pathway toward the building integrated thermoelectric air conditioning

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-03-09 DOI: 10.1016/j.adapen.2025.100218

Haowen Liu , Limei Shen , Yunhai Li , Xudong Zhao , Guiqiang Li , Zeyu Liu , Hongxing Yang

Due to structural limitations, the hot and cold sides of conventional thermoelectric coolers (TECs) are fully integrated, making it challenging to directly incorporate TECs into building facades or ceilings to utilize natural ventilation from the building exterior assisting cooling the hot junction. This constraint renders TECs unsuitable for direct application in building façade. To overcome these challenges, an innovative separately-configured thermoelectric cooler (SC-TEC) has been developed. This original design enables the direct integration of TECs into building façades for air conditioning while utilizing the outdoor environment as auxiliary cooling for the TEC's hot side, thereby enhancing overall system performance. Our preliminary study showed that, in a TECs-ceiling system, the novel SC-TEC achieves a 13 % higher cooling capacity compared to a traditional TEC-ceiling. The unit cooling output increased from 16.66 W/m² to 18.82 W/m². And the temperature profiles shows that the cooling capacity of the SC-TEC could be further enhanced with a higher-performance connecting material. Given its advantages, such as no moving parts, noiseless operation, and efficient heat transfer, the SC-TEC has potential to open up new research direction in the building-TEC sector.

由于结构限制，传统热电半导体制冷片（TECs）的冷热两侧是完全一体的，因此将 TECs 直接安装在建筑物外墙或天花板上，利用建筑物外部的自然通风来冷却热交界处，具有很大的挑战性。这种限制使得 TEC 不适合直接应用于建筑外墙。为了克服这些挑战，我们开发了一种创新的独立配置热电冷却器（SC-TEC）。这种独创的设计可将热电半导体制冷片直接集成到建筑幕墙中用于空调，同时利用室外环境作为热电半导体制冷片热侧的辅助冷却，从而提高整个系统的性能。我们的初步研究表明，与传统的 TEC 天花板系统相比，新型 SC-TEC 在 TEC 天花板系统中的冷却能力提高了 13%。单位冷却输出从 16.66 W/m² 增加到 18.82 W/m²。温度曲线显示，如果使用性能更高的连接材料，SC-TEC 的冷却能力还能进一步提高。鉴于 SC-TEC 无运动部件、无噪音运行和高效传热等优点，它有可能为建筑电子技术领域开辟新的研究方向。

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引用次数: 0

Decarbonizing energy: Plastic waste trade for zero waste 2040

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-03-01 DOI: 10.1016/j.adapen.2025.100216

Xiang Zhao , Fengqi You

Plastics are essential to human activities but also drive climate burdens and global pollution from energy-intensive material production and waste treatment. This study proposes and evaluates sustainable technology roadmaps under energy transitions to non-fossils to decarbonize the plastic life cycle and mitigate pollution across 202 global countries from 2024 to 2060. The results show that substituting plastic use, combined with advanced chemical recycling and carbon capture utilization powered by renewables, minimizes waste generation and pollution. In North America and Europe, replacing 56.7 % of plastics with glass, metal, and biodegradable alternatives coupled with chemical recycling can achieve zero annual waste by 2040 or 2035 with biomass-powered carbon capture and utilization. In African and Southeast Asian countries, this net-zero waste goal will be delayed to 2055 due to excessive plastic waste from global trade imports. Strategies including a 50 % cross-border tariff increment on plastic waste export and promoting alternative material use can reduce 87.45 % of global waste trade volume and surpluses to developed countries. This study advances the existing local and global plastic pollution mitigation strategies integrating energy decarbonization and transition to strengthen the United Nations Global Plastic Treaty towards minimum plastic pollution.

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引用次数: 0

Impact of foresight horizons on energy system decarbonization pathways

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-02-26 DOI: 10.1016/j.adapen.2025.100217

Rachel Maier , Johannes Behrens , Maximilian Hoffmann , Felix Kullmann , Jann M. Weinand , Detlef Stolten

Energy system optimization models often assume a perfect foresight of all defining future influences, assuming full knowledge of technological and socio-political developments over a long time horizon. In addition, many models impose annual emission reductions to prevent short-sighted decisions from deviating from these reduction paths. In this paper, we analyze different approaches to model foresight horizons and compare the influence of a perfect foresight approach with a rolling horizon and a purely myopic approach. For the first time, we explore the combination of incrementally increasing limited foresight horizons combined with a cumulative emission budget. Results from a case study of the German energy system indicate that the foresight horizon significantly affects the decarbonization trajectory. Short-sighted decisions that only consider the next five years even lead to infeasible pathways, failing the transition of the energy system by missing complying with the emission constraint. In contrast, long-sighted decision-making shows earlier investments in renewable energies and decarbonization, which can save billions of euros. Short-term cost-efficiency conflicts with long-term decarbonization goals, underscoring the importance of incorporating long-term perspectives into policy and decision-making, as well-defined decarbonization plans to achieve sustainable climate targets. This study underscores the importance of well-defined annual emissions targets and their achievement, highlighting the potential consequences of missing emissions targets.

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引用次数: 0

Reinforcement learning for vehicle-to-grid: A review

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-02-08 DOI: 10.1016/j.adapen.2025.100214

Hongbin Xie , Ge Song , Zhuoran Shi , Jingyuan Zhang , Zhenjia Lin , Qing Yu , Hongdi Fu , Xuan Song , Haoran Zhang

The rapid development of Vehicle-to-Grid technology has played a crucial role in peak shaving and power scheduling within the power grid. However, with the random integration of a large number of electric vehicles into the grid, the uncertainty and complexity of the system have significantly increased, posing substantial challenges to traditional algorithms. Reinforcement learning has shown great potential in addressing these high-dimensional dynamic scheduling optimization problems. However, there is currently a lack of comprehensive analysis and systematic understanding of reinforcement learning applications in Vehicle-to-Grid, which limits the further development of this technology in the Vehicle-to-Grid domain. To this end, this review systematically analyzes the application of reinforcement learning in Vehicle-to-Grid from the perspective of different stakeholders, including the power grid, aggregators, and electric vehicle users, and clarifies the effectiveness and mechanisms of reinforcement learning in addressing the uncertainty in power scheduling. Based on a comprehensive review of the development trajectory of reinforcement learning in Vehicle-to-Grid applications, this paper proposes a structured framework for method classification and application analysis. It also highlights the major challenges currently faced by reinforcement learning in the Vehicle-to-Grid domain and provides targeted directions for future research. Through this systematic review of reinforcement learning applications in Vehicle-to-Grid, the paper aims to provide relevant references for subsequent studies.

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引用次数: 0

Advancements and future outlook of Artificial Intelligence in energy and climate change modeling

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-28 DOI: 10.1016/j.adapen.2025.100211

Mobolaji Shobanke, Mehul Bhatt, Ekundayo Shittu

This paper explores the employment of artificial intelligence and machine learning to decipher strategic responses to incidences of climate change and to inform the management of energy systems. Given the increasing global dependence on sustainable and efficient energy solutions and the rise of artificial intelligence and machine learning, it has become imperative to evaluate existing routines in energy and climate change modeling to identify areas for further application. The process of conducting a systematic review of the contemporary literature highlights significant advances in optimization and predictive analytics within energy and climate change modeling systems driven by artificial intelligence and machine learning. This paper contributes to cutting-edge research on energy innovation, i.e., through the examination of the applications of artificial intelligence and machine learning in energy modeling and climate change assessments. The article bridges the gaps between research, development, and implementation with significant insights into the broader applications of artificial intelligence and machine learning in the analysis of future energy transitions and climate change mitigation and adaptation.

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引用次数: 0

Advancing building facade solar potential assessment through AIoT, GIS, and meteorology synergy

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-27 DOI: 10.1016/j.adapen.2025.100212

Kechuan Dong , Qing Yu , Zhiling Guo , Jian Xu , Hongjun Tan , Haoran Zhang , Jinyue Yan

The assessment of building solar potential plays a pivotal role in Building Integrated Photovoltaics (BIPV) and urban energy systems. While current evaluations predominantly focus on rooftop solar resources, a comprehensive analysis of building facade BIPV potential is often lacking. This study presents an innovative methodology that harnesses state-of-the-art Artificial Intelligence of Things (AIoT) techniques, Geographic Information Systems (GIS), and Meteorology to develop a model for accurately estimating spatial–temporal building facade BIPV potential considering 3 Dimension (3D) shading effect. Here, we introduce a zero-shot Deep Learning framework for detailed parsing of facade elements, utilizing cutting-edge techniques in Large-scale Segment Anything Model (SAM), Grounding DINO (Detection Transformer with improved denoising anchor boxes), and Stable Diffusion. Considering urban morphology, 3D shading impacts, and multi-source weather data enables a meticulous estimation of solar potential for each facade element. The experimental findings, gathered from a range of buildings across four countries and an entire street in Japan, highlight the effectiveness and applicability of our approach in conducting comprehensive analyses of facade solar potential. These results underscore the critical importance of integrating shadow effects and detailed facade elements to ensure accurate estimations of PV potential.

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引用次数: 0

Integrating water availability for electrolysis into energy system modeling

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-27 DOI: 10.1016/j.adapen.2025.100208

Julian Walter , Lina Fischer , Sandra Venghaus , Albert Moser

In recent years, temperature records have been broken all over the world and the global temperature keeps rising. As a result, fresh water availability will diminish ever more and more due to droughts and extreme weather events. Water is a key part of many central aspects of life but will also become important in the future for electrolysis to synthesize hydrogen, a promising energy carrier in energy systems for the transition from fossil to renewable energy. Current energy system optimization models neglect water as an input for electrolysis when focusing on electricity. In this study, we present a method for implementing water as an input in energy system optimization models, with constraints for freshwater availability and seawater processing. We apply our method to one scenario and investigate the impact on the European energy system with highly-detailed spatial and temporal resolutions. The results indicate a relocation of electrolysis capacities of 10% and an increase of methane imports and methanation capacities. The effects suggest that water should be considered in energy system optimization in the future.

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引用次数: 0

A review of participatory modelling techniques for energy transition scenarios

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-26 DOI: 10.1016/j.adapen.2025.100215

Jair K.E.K. Campfens , Mert Duygan , Claudia R. Binder

Energy transitions are pivotal for sustainability, yet their complexity and uncertainty pose significant challenges for effective planning and implementation. Participatory modelling has emerged as a promising approach to support these transitions, as it involves incorporating stakeholders' perspectives into models and policy designs, which helps integrate their mental models and preferences into simulations. This paper reviews the current state of participatory modelling in transition research for energy scenarios. Drawing on a comprehensive literature review and semi-structured interviews, we extract findings by evaluating participatory modelling techniques against criteria such as normative dimensions, non-linearity, actors and agency, uncertainty and emergence. Findings reveal that techniques like Cross-Impact Balance analysis and Fuzzy Cognitive Mapping excel in incorporating normative aspects and capturing diverse actor perspectives, yet they face challenges in addressing non-linearity and uncertainty. Bayesian Networks and Agent-Based Models are strong in managing uncertainty and modelling emergent behaviours but show limitations in normative aspects. Our findings provide a foundation for scholars and practitioners in the field of socio-technical energy transitions to select participatory modelling techniques best suited to their specific research contexts. This review also highlights gaps between theoretical potential and practical application of participatory modelling techniques. Bridging these gaps requires methodological advancement and a more rigorous application in empirical studies. To this end, future directions for blending techniques are discussed to better address the complexities of energy transitions.

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引用次数: 0